If you are looking to ‘do’ the focus, focus, focus thing day in day out and getting stressed, you are not alone. The over-emphasis on focusing on tasks to achieve daily goals has become boring. Useful but boring. What can be another, simpler way? Is there even such a thing.
The answer unravels by asking ourselves, what is/are the thing/s that detracts us from focusing. More commonly referred to as distractions, we all succumb to its seductive, insidious embrace to varying degrees and once we realize it, it often leaves us annoyed with ourselves, embarrassed, anxious about the irreversible loss of time. If you see the meanings of the word distraction from Merriam-webster you’ll know why.
- A state of wildly excited activity or emotion
- The act or activity of providing pleasure or amusement
- A state of mental uncertainty
With the lethal combination of mobile phones and internet we can see how much of wild excitation, wild emotion, pleasurable activity and amusement is within arm’s reach…nay in our palms.
Contrast this to the meaning of the word “goal“: Again Merriam-Webster: “something that one hopes or intends to accomplish”. That’s it. Lets check out “task“: ‘a piece of work that needs to be done regularly’. Can there be more boring definition!!
Contrast these two words even when combined, with ‘distraction‘ and you can clearly see who wins.
There is no way that humans would naturally gravitate to focus on tasks when we are naturally pre-disposed to seeking the dopamine rush of distractions.
So is it hopeless? Maybe not..
In Machine learning, much time and effort is spent upfront in ‘cleaning’ the data. Fancifully called exploratory data analysis(EDA). In terms of actual coding one can argue that more than half the overall time taken to develop a machine learning(ML) model, could get consumed just in EDA. One of the major outcomes of doing this is to help the algorithm know what to focus on. For making this happen we study a whole bunch of ‘distractions’ (columns of attributes/dependant variables/themes etc.) very minutely sometimes and then and only then, are we able to eliminate it. There’s a moral here for humans. i.e. we need to study our own distractions and get conscious of it first, before we can more easily and fluently train our mind, just as we train the ML model, on our boring ‘tasks’. The good news is: as in ML, this is a one time exercise.
So here are the 5 things:
- Sit in a place. Compose yourself and make a long list of the things that you feel routinely distract you from achieving what you set out to achieve every day.
- Study each item on this list and label them as WMC (within my control) or OMC (outside my control)
- First look at the OMC labeled items and look at which of these are mitigatable. e.g. a loud colleague distracts. This is an OMC. So look to find a quiet room or a different desk/ area in your workplace to get the more important things done while that colleague is around. Plan to do this. Like this, systematically go down the list of OMCs and think up a mitigation for every OMCs on your list — consciously.
- Look at your WMCs. We need to treat these variables with more nuance than OMCs. We give them categorical values 0-Stop, 1-Reduce. There is no 3rd category of Continue. Sorry. And you are the judge. If you categorized any WMC as 1 give yourself a meaningful and actionable handle to actually reduce it i.e. if you are given to watching korean serials on the sly endlessly, give yourself the pleasure of watching it for say 10 mins, and do it without guilt. A measured dose of distractions are good for our well-being. But also add a dis-incentive that it could take you upto 20 mins to shake-off the hangover thoughts. So budget for it. This means you’ll need to budget 10+20 i.e 30 mins time off for a 10 min permitted distraction.
- Keep this list in a place you can refer to from time to time. More during the early ‘training’ phase. So you can iteratively arrive at a happy balance.
- Here’s the interesting parallel. ML models too, are developed iteratively until the desired accuracy and outcomes are demonstrated. Once you train the model that way and test it, it will run exactly the way you trained it, in the real world. In a very disciplined and trustable way. Likewise for us humans. Once you try out the above 4 steps and tweak it day by day, you will get to a point where you have trained yourself to run your rest of the day smoothly. In a very disciplined and trustable way and for every day of the rest of your life.
And yeah, by the way, you’ll end up accomplishing your tasks alright. Without the cliched focus and consequent stress! Won’t hurt to try. Try it.
Give this approach a shot and share some of your OMCs and WMCs with the other readers and how you will master it.
